code: def multinomial(
      random_state,
      n,
      pvals,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      """
      Draw samples from a multinomial distribution.
  
      The multinomial distribution is a multivariate generalisation of the
      binomial distribution.  Take an experiment with one of ``p``
      possible outcomes.  An example of such an experiment is throwing a dice,
      where the outcome can be 1 through 6.  Each sample drawn from the
      distribution represents `n` such experiments.  Its values,
      ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the
      outcome was ``i``.
  
      Parameters
      ----------
      n : int
          Number of experiments.
      pvals : sequence of floats, length p
          Probabilities of each of the ``p`` different outcomes.  These
          should sum to 1 (however, the last element is always assumed to
          account for the remaining probability, as long as
          ``sum(pvals[:-1]) <= 1)``.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  Default is None, in which case a
          single value is returned.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor
          The drawn samples, of shape *size*, if that was provided.  If not,
          the shape is ``(N,)``.
  
          In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
          value drawn from the distribution.
  
      Examples
      --------
      Throw a dice 20 times:
  
      >>> import mars.tensor as mt
  
      >>> mt.random.multinomial(20, [1/6.]*6, size=1).execute()
      array([[4, 1, 7, 5, 2, 1]])
  
      It landed 4 times on 1, once on 2, etc.
  
      Now, throw the dice 20 times, and 20 times again:
  
      >>> mt.random.multinomial(20, [1/6.]*6, size=2).execute()
      array([[3, 4, 3, 3, 4, 3],
             [2, 4, 3, 4, 0, 7]])
  
      For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,
      we threw 2 times 1, 4 times 2, etc.
  
      A loaded die is more likely to land on number 6:
  
      >>> mt.random.multinomial(100, [1/7.]*5 + [2/7.]).execute()
      array([11, 16, 14, 17, 16, 26])
  
      The probability inputs should be normalized. As an implementation
      detail, the value of the last entry is ignored and assumed to take
      up any leftover probability mass, but this should not be relied on.
      A biased coin which has twice as much weight on one side as on the
      other should be sampled like so:
  
      >>> mt.random.multinomial(100, [1.0 / 3, 2.0 / 3]).execute()  # RIGHT
      array([38, 62])
  
      not like:
  
      >>> mt.random.multinomial(100, [1.0, 2.0]).execute()  # WRONG
      array([100,   0])
      """
      n = int(n)
      pvals = tuple(pvals)
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .multinomial(n, pvals, size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorMultinomial(
          n=n, pvals=pvals, seed=seed, size=size, gpu=gpu, dtype=dtype
      )
      return op(chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/multinomial.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import FieldTypes, Int64Field, TupleField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: multinomial(random_state, n, pvals, size, chunk_size, gpu, dtype) and returns: op